This is the supplementary material to an invited commentary for Basole et al. (2021). We provide all code that are used to generate the figures in the commentary in addition to other supplementary figures (and its code). To see the code, click on the CODE button. You can also download the whole R Markdown file from the drop down menu on the top right corner.

List of figures

Code to load library and data
library(tidyverse)
library(ggtext)
library(patchwork)
library(readxl)
library(nullabor)
library(here)
library(janitor)
library(scales)
#theme_set(theme_classic())
knitr::opts_chunk$set(fig.path = "images/",
                      dev = c("png", "pdf", "svg"),
                      cache = TRUE,
                      cache.path = "cache/")
df_full <- read_xlsx(here("data/MaskedCoverage-Fig3.xlsx")) %>% 
  clean_names() %>% 
  add_row(state = c("OR", "WY", "SD", "WV", "DC", "AL")) %>% 
  mutate(row = case_when(
    state %in% c("ME") ~ 1L,
    state %in% c("VT", "NH") ~ 2L,
    state %in% c("WA", "ID", "MT", "ND", "MN", "IL", "WI", "MI", "NY", "RI", "MA") ~ 3L,
    state %in% c("OR", "NV", "WY", "SD", "IA", "IN", "OH", "PA", "NJ", "CT") ~ 4L,
    state %in% c("CA", "UT", "CO", "NE", "MO", "KY", "WV", "VA", "MD", "DE") ~ 5L,
    state %in% c("AZ", "NM", "KS", "AR", "TN", "NC", "SC", "DC") ~ 6L,
    state %in% c("OK", "LA", "MS", "AL", "GA") ~ 7L,
    state %in% c("TX", "FL") ~ 8L,
                         TRUE ~ 0L),
    col = case_when(
      state %in% c("WA", "OR", "CA") ~ 1L,
      state %in% c("ID", "NV", "UT", "AZ") ~ 2L,
      state %in% c("MT", "WY", "CO", "NM") ~ 3L,
      state %in% c("ND", "SD", "NE", "KS", "OK", "TX") ~ 4L,
      state %in% c("MN", "IA", "MO", "AR", "LA") ~ 5L,
      state %in% c("IL", "IN", "KY", "TN", "MS") ~ 6L,
      state %in% c("WI", "OH", "WV", "NC", "AL") ~ 7L,
      state %in% c("MI", "PA", "VA", "SC", "GA") ~ 8L,
      state %in% c("NY", "NJ", "MD", "DC", "FL") ~ 9L,
      state %in% c("VT", "RI", "CT", "DE") ~ 10L,
      state %in% c("ME", "NH", "MA") ~ 11L,
      TRUE ~ 0L
    ))

df_miss <- df_full %>% 
  filter(!is.na(readmission_rate))
g1 <- ggplot(df_miss, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = readmission_rate * 100), alpha = 0.8) +
  geom_text(data = df_full, aes(label = state), color = "black", nudge_y = 0.05) +
  geom_text(aes(label = percent(readmission_rate, 0.01)), nudge_y = -0.1, size = 2.5) +
  theme_void() +
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$readmission_rate * 100), mid = "#E7D9C6") +
  scale_size(range = c(3, 30)) +
  scale_y_reverse() +
  theme(plot.margin = margin(r = 30)) +
  labs(color = "Readmission",
       size = "Coverage")

g2 <- ggplot(df_miss, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = colorectal_cancer_screenings), alpha = 0.8) +
  geom_text(data = df_full, aes(label = state), color = "black", nudge_y = 0.05) +
  geom_text(aes(label = percent(colorectal_cancer_screenings/100, 0.01)), nudge_y = -0.1, size = 2.5) +
  theme_void() +
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$colorectal_cancer_screenings), mid = "#E7D9C6") +
  scale_size(range = c(3, 30)) +
  scale_y_reverse()  +
  labs(color = "Cancer Screening",
       size = "Coverage")

g1 + g2 + plot_layout(guides = "collect") 
This figure recreates Figure 3 in Basole et al. (2021).

Figure S1: This figure recreates Figure 3 in Basole et al. (2021).

theme_set(theme_classic())
g1 <- ggplot(df_miss, aes(coverage_obscured * 100, colorectal_cancer_screenings)) +
  geom_point() +
  labs(x = "Coverage (%)", y = "Cancer Screening (%)") +
  geom_smooth(method = loess, formula = y ~ x) +
  annotate("richtext", x = 80, y = 73, label.color = NA, fill = "transparent", label = glue::glue("R<sup>2</sup> = {scales::comma(cor(df_miss$coverage_obscured, df_miss$colorectal_cancer_screenings), 0.001)}")) 

g2 <- ggplot(df_miss, aes(coverage_obscured * 100, readmission_rate * 100)) +
  geom_point() +
  labs(x = "Coverage (%)", y = "Readmission (%)") +
  geom_smooth(method = loess, formula = y ~ x) +
  annotate("richtext", x = 80, y = 15.3, label.color = NA, fill = "transparent", label = glue::glue("R<sup>2</sup> = {scales::comma(cor(df_miss$coverage_obscured, df_miss$readmission_rate), 0.001)}")) 

g1 + g2 
This is an alternative graph design for Figure S1.

Figure S2: This is an alternative graph design for Figure S1.

set.seed(2021)
lineup_data <- null_permute("colorectal_cancer_screenings") %>% 
  lineup(true = df_miss, n = 20, pos = 3)
plot_lineup_theirs <- ggplot(lineup_data, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = colorectal_cancer_screenings), alpha = 0.8) +
  theme_void() + 
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$colorectal_cancer_screenings), mid = "#E7D9C6") +
  scale_size(range = c(1, 5)) +
  scale_y_reverse(expand = c(0.1, 0.2))  +
  guides(color = "none", size = "none") + 
  facet_wrap(~.sample, ncol = 5) +
  scale_x_continuous(expand = c(0.1, 0.1)) + 
  theme(legend.position = "bottom",
        strip.text = element_text(size = 18, margin = margin(t = 3, b = 3)),
        strip.background = element_rect(color = "black", size = 1.5))

plot_lineup_theirs
The lineup for the tile grid plot.

Figure S3: The lineup for the tile grid plot.

plot_lineup_ours <- ggplot(lineup_data, aes(coverage_obscured * 100, colorectal_cancer_screenings)) +
  geom_point() +  
  geom_smooth(method = loess, formula = y ~ x) +
  facet_wrap(~.sample, ncol = 5) +
  scale_x_continuous(expand = c(0.1, 0.1)) + 
  theme(legend.position = "bottom",
        strip.text = element_text(size = 18, margin = margin(t = 3, b = 3)),
        strip.background = element_rect(color = "black", size = 1.5),
        axis.text = element_blank(),
        axis.title = element_blank(),
        axis.line = element_blank(),
        axis.ticks.length = unit(0, "pt"))

plot_lineup_ours
The lineup for the scatter plot.

Figure S4: The lineup for the scatter plot.

Same plots with higher associations between variables

The following are plots based on data that purposely modifies cancer screening to induce a higher association with the coverage. This higher association is induced (as shown in the code below) by rearranging data by the coverage and modifying the cancer screening percentage so that it is ordered from low to high.

df_false <- df_miss %>% 
  arrange(coverage_obscured) %>% 
  mutate(colorectal_cancer_screenings = sort(colorectal_cancer_screenings))

lineup_false_data <- null_permute("colorectal_cancer_screenings") %>% 
  lineup(true = df_false, n = 20, pos = 5)
plot_lineup_theirs %+% lineup_false_data
Which plot looks the most strikingly different to you?

Figure S5: Which plot looks the most strikingly different to you?

plot_lineup_ours %+% lineup_false_data
The above shows a lineup for data that was purposely manipulated so that two variables have a higher association. How easy was it to spot the data plot compared to Figure S5?

Figure S6: The above shows a lineup for data that was purposely manipulated so that two variables have a higher association. How easy was it to spot the data plot compared to Figure S5?

Acknowledgement

We thank Basole et al. (2021) for supplying us the synthetic data to draw the above plots.

Reference

Session Information
sessioninfo::session_info()
## ─ Session info ────────────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.0.1 (2020-06-06)
##  os       macOS  10.16                
##  system   x86_64, darwin17.0          
##  ui       RStudio                     
##  language (EN)                        
##  collate  en_AU.UTF-8                 
##  ctype    en_AU.UTF-8                 
##  tz       Australia/Melbourne         
##  date     2021-09-19                  
## 
## ─ Packages ────────────────────────────────────────────────────────────────────────
##  package     * version date       lib source                           
##  assertthat    0.2.1   2019-03-21 [2] CRAN (R 4.0.0)                   
##  backports     1.2.1   2020-12-09 [1] CRAN (R 4.0.2)                   
##  bookdown      0.22.17 2021-08-07 [1] Github (rstudio/bookdown@9615b14)
##  broom         0.7.9   2021-07-27 [1] CRAN (R 4.0.2)                   
##  bslib         0.2.5   2021-05-12 [1] CRAN (R 4.0.1)                   
##  cellranger    1.1.0   2016-07-27 [2] CRAN (R 4.0.0)                   
##  class         7.3-19  2021-05-03 [2] CRAN (R 4.0.2)                   
##  cli           3.0.1   2021-07-17 [1] CRAN (R 4.0.2)                   
##  cluster       2.1.2   2021-04-17 [2] CRAN (R 4.0.2)                   
##  codetools     0.2-18  2020-11-04 [2] CRAN (R 4.0.1)                   
##  colorspace    2.0-1   2021-05-04 [1] CRAN (R 4.0.2)                   
##  crayon        1.4.1   2021-02-08 [1] CRAN (R 4.0.2)                   
##  DBI           1.1.1   2021-01-15 [1] CRAN (R 4.0.2)                   
##  dbplyr        2.1.1   2021-04-06 [1] CRAN (R 4.0.2)                   
##  DEoptimR      1.0-8   2016-11-19 [2] CRAN (R 4.0.0)                   
##  digest        0.6.27  2020-10-24 [1] CRAN (R 4.0.2)                   
##  diptest       0.76-0  2021-05-04 [2] CRAN (R 4.0.2)                   
##  dplyr       * 1.0.7   2021-06-18 [1] CRAN (R 4.0.2)                   
##  ellipsis      0.3.2   2021-04-29 [1] CRAN (R 4.0.2)                   
##  evaluate      0.14    2019-05-28 [2] CRAN (R 4.0.0)                   
##  fansi         0.5.0   2021-05-25 [1] CRAN (R 4.0.2)                   
##  farver        2.1.0   2021-02-28 [1] CRAN (R 4.0.2)                   
##  flexmix       2.3-17  2020-10-12 [1] CRAN (R 4.0.2)                   
##  forcats     * 0.5.1   2021-01-27 [1] CRAN (R 4.0.2)                   
##  fpc           2.2-9   2020-12-06 [2] CRAN (R 4.0.2)                   
##  fs            1.5.0   2020-07-31 [1] CRAN (R 4.0.2)                   
##  generics      0.1.0   2020-10-31 [2] CRAN (R 4.0.2)                   
##  ggplot2     * 3.3.3   2020-12-30 [1] CRAN (R 4.0.1)                   
##  ggtext      * 0.1.1   2020-12-17 [1] CRAN (R 4.0.2)                   
##  glue          1.4.2   2020-08-27 [1] CRAN (R 4.0.2)                   
##  gridtext      0.1.4   2020-12-10 [1] CRAN (R 4.0.2)                   
##  gtable        0.3.0   2019-03-25 [2] CRAN (R 4.0.0)                   
##  haven         2.4.1   2021-04-23 [2] CRAN (R 4.0.2)                   
##  here        * 1.0.1   2020-12-13 [2] CRAN (R 4.0.2)                   
##  highr         0.9     2021-04-16 [2] CRAN (R 4.0.2)                   
##  hms           1.1.0   2021-05-17 [1] CRAN (R 4.0.2)                   
##  htmltools     0.5.1.1 2021-01-22 [1] CRAN (R 4.0.2)                   
##  httpuv        1.6.1   2021-05-07 [2] CRAN (R 4.0.2)                   
##  httr          1.4.2   2020-07-20 [1] CRAN (R 4.0.2)                   
##  janitor     * 2.1.0   2021-01-05 [2] CRAN (R 4.0.2)                   
##  jquerylib     0.1.4   2021-04-26 [1] CRAN (R 4.0.2)                   
##  jsonlite      1.7.2   2020-12-09 [1] CRAN (R 4.0.2)                   
##  kernlab       0.9-29  2019-11-12 [2] CRAN (R 4.0.0)                   
##  knitr         1.33    2021-04-24 [1] CRAN (R 4.0.2)                   
##  labeling      0.4.2   2020-10-20 [1] CRAN (R 4.0.2)                   
##  later         1.2.0   2021-04-23 [1] CRAN (R 4.0.2)                   
##  lattice       0.20-44 2021-05-02 [2] CRAN (R 4.0.2)                   
##  lifecycle     1.0.0   2021-02-15 [1] CRAN (R 4.0.2)                   
##  lubridate     1.7.10  2021-02-26 [1] CRAN (R 4.0.2)                   
##  magrittr      2.0.1   2020-11-17 [1] CRAN (R 4.0.2)                   
##  markdown      1.1     2019-08-07 [2] CRAN (R 4.0.0)                   
##  MASS          7.3-54  2021-05-03 [1] CRAN (R 4.0.2)                   
##  Matrix        1.3-3   2021-05-04 [2] CRAN (R 4.0.2)                   
##  mclust        5.4.7   2020-11-20 [2] CRAN (R 4.0.2)                   
##  mgcv          1.8-35  2021-04-18 [2] CRAN (R 4.0.2)                   
##  mime          0.11    2021-06-23 [1] CRAN (R 4.0.2)                   
##  modelr        0.1.8   2020-05-19 [2] CRAN (R 4.0.0)                   
##  modeltools    0.2-23  2020-03-05 [2] CRAN (R 4.0.0)                   
##  moments       0.14    2015-01-05 [2] CRAN (R 4.0.0)                   
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##  Rcpp          1.0.7   2021-07-07 [1] CRAN (R 4.0.2)                   
##  readr       * 2.0.1   2021-08-10 [1] CRAN (R 4.0.2)                   
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## 
## [1] /Users/etan0038/Library/R/4.0/library
## [2] /Library/Frameworks/R.framework/Versions/4.0/Resources/library


Basole, Rahul C., Elliot Bendoly, Aravind Chandrasekaran, and Kevin Linderman. 2021. “Visualization in Operations Management Research.” INFORMS Journal on Data Science (to appear).
---
title: "Supplementary material for \"Uprooting sub-standard visualisation practices for decision-making in operational management\""
author:
  - name: Emi Tanaka
    affiliation: Department of Econometrics and Business Statistics, Monash University, Melbourne, VIC 3800
    email: emi.tanaka@monash.edu
  - name: Jessica Wai Yin Leung
    affiliation: Department of Econometrics and Business Statistics, Monash University, Melbourne, VIC 3800
    email: jessica.leung@monash.edu
  - name: Dianne Cook
    affiliation: Department of Econometrics and Business Statistics, Monash University, Melbourne, VIC 3800
    email: dicook@monash.edu
bibliography: references.bib 
output:
  bookdown::html_document2:
    code_folding: "hide"
    theme: "paper"
    code_download: true
    number_sections: false
---

This is the supplementary material to an invited commentary for @basole2021. We provide all code that are used to generate the figures in the commentary in addition to other supplementary figures (and its code). To see the code, click on the CODE button. You can also download the whole R Markdown file from the drop down menu on the top right corner.


**List of figures** 

* [Figure S1](#fig:mimic-original): Recreating Figure 3 of @basole2021 using `ggplot2`. 
* [Figure S2](#fig:fig3-alt): An alternative design to Figure 3 of @basole2021.
* [Figure S3](#fig:lineup-theirs) Lineup for the tile grid plot used in Figure 3 of @basole2021.
* [Figure S4](#fig:lineup-ours) Lineup for the scatter plot. 
* [Figure S5](#fig:lineup-theirs-false) Lineup for the tile grid plot on data with purposely high association.
* [Figure S6](#fig:lineup-ours-false) Lineup for the scatter plot on data with purposely high association.

<details>
<summary>Code to load library and data</summary>
```{r setup, message = FALSE, warning = FALSE, class.source = 'fold-show'}
library(tidyverse)
library(ggtext)
library(patchwork)
library(readxl)
library(nullabor)
library(here)
library(janitor)
library(scales)
#theme_set(theme_classic())
knitr::opts_chunk$set(fig.path = "images/",
                      dev = c("png", "pdf", "svg"),
                      cache = TRUE,
                      cache.path = "cache/")
```


```{r data, class.source = 'fold-show'}
df_full <- read_xlsx(here("data/MaskedCoverage-Fig3.xlsx")) %>% 
  clean_names() %>% 
  add_row(state = c("OR", "WY", "SD", "WV", "DC", "AL")) %>% 
  mutate(row = case_when(
    state %in% c("ME") ~ 1L,
    state %in% c("VT", "NH") ~ 2L,
    state %in% c("WA", "ID", "MT", "ND", "MN", "IL", "WI", "MI", "NY", "RI", "MA") ~ 3L,
    state %in% c("OR", "NV", "WY", "SD", "IA", "IN", "OH", "PA", "NJ", "CT") ~ 4L,
    state %in% c("CA", "UT", "CO", "NE", "MO", "KY", "WV", "VA", "MD", "DE") ~ 5L,
    state %in% c("AZ", "NM", "KS", "AR", "TN", "NC", "SC", "DC") ~ 6L,
    state %in% c("OK", "LA", "MS", "AL", "GA") ~ 7L,
    state %in% c("TX", "FL") ~ 8L,
                         TRUE ~ 0L),
    col = case_when(
      state %in% c("WA", "OR", "CA") ~ 1L,
      state %in% c("ID", "NV", "UT", "AZ") ~ 2L,
      state %in% c("MT", "WY", "CO", "NM") ~ 3L,
      state %in% c("ND", "SD", "NE", "KS", "OK", "TX") ~ 4L,
      state %in% c("MN", "IA", "MO", "AR", "LA") ~ 5L,
      state %in% c("IL", "IN", "KY", "TN", "MS") ~ 6L,
      state %in% c("WI", "OH", "WV", "NC", "AL") ~ 7L,
      state %in% c("MI", "PA", "VA", "SC", "GA") ~ 8L,
      state %in% c("NY", "NJ", "MD", "DC", "FL") ~ 9L,
      state %in% c("VT", "RI", "CT", "DE") ~ 10L,
      state %in% c("ME", "NH", "MA") ~ 11L,
      TRUE ~ 0L
    ))

df_miss <- df_full %>% 
  filter(!is.na(readmission_rate))
```
</details>

(ref:mimicary) This figure recreates Figure 3 in @basole2021.

```{r mimic-original, fig.height = 8, fig.width = 18, fig.cap = "(ref:mimicary)"}
g1 <- ggplot(df_miss, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = readmission_rate * 100), alpha = 0.8) +
  geom_text(data = df_full, aes(label = state), color = "black", nudge_y = 0.05) +
  geom_text(aes(label = percent(readmission_rate, 0.01)), nudge_y = -0.1, size = 2.5) +
  theme_void() +
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$readmission_rate * 100), mid = "#E7D9C6") +
  scale_size(range = c(3, 30)) +
  scale_y_reverse() +
  theme(plot.margin = margin(r = 30)) +
  labs(color = "Readmission",
       size = "Coverage")

g2 <- ggplot(df_miss, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = colorectal_cancer_screenings), alpha = 0.8) +
  geom_text(data = df_full, aes(label = state), color = "black", nudge_y = 0.05) +
  geom_text(aes(label = percent(colorectal_cancer_screenings/100, 0.01)), nudge_y = -0.1, size = 2.5) +
  theme_void() +
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$colorectal_cancer_screenings), mid = "#E7D9C6") +
  scale_size(range = c(3, 30)) +
  scale_y_reverse()  +
  labs(color = "Cancer Screening",
       size = "Coverage")

g1 + g2 + plot_layout(guides = "collect") 
```

(ref:fig3-alt) This is an alternative graph design for [Figure S1](#fig:mimic-original).

```{r fig3-alt, fig.height = 4, fig.width = 8, fig.cap = "(ref:fig3-alt)"}
theme_set(theme_classic())
g1 <- ggplot(df_miss, aes(coverage_obscured * 100, colorectal_cancer_screenings)) +
  geom_point() +
  labs(x = "Coverage (%)", y = "Cancer Screening (%)") +
  geom_smooth(method = loess, formula = y ~ x) +
  annotate("richtext", x = 80, y = 73, label.color = NA, fill = "transparent", label = glue::glue("R<sup>2</sup> = {scales::comma(cor(df_miss$coverage_obscured, df_miss$colorectal_cancer_screenings), 0.001)}")) 

g2 <- ggplot(df_miss, aes(coverage_obscured * 100, readmission_rate * 100)) +
  geom_point() +
  labs(x = "Coverage (%)", y = "Readmission (%)") +
  geom_smooth(method = loess, formula = y ~ x) +
  annotate("richtext", x = 80, y = 15.3, label.color = NA, fill = "transparent", label = glue::glue("R<sup>2</sup> = {scales::comma(cor(df_miss$coverage_obscured, df_miss$readmission_rate), 0.001)}")) 

g1 + g2 
```

```{r lineup-data}
set.seed(2021)
lineup_data <- null_permute("colorectal_cancer_screenings") %>% 
  lineup(true = df_miss, n = 20, pos = 3)
```

(ref:lineup-theirs) The lineup for the tile grid plot.

```{r lineup-theirs, fig.height = 10, fig.width = 10, fig.cap = "(ref:lineup-theirs)"}
plot_lineup_theirs <- ggplot(lineup_data, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = colorectal_cancer_screenings), alpha = 0.8) +
  theme_void() + 
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$colorectal_cancer_screenings), mid = "#E7D9C6") +
  scale_size(range = c(1, 5)) +
  scale_y_reverse(expand = c(0.1, 0.2))  +
  guides(color = "none", size = "none") + 
  facet_wrap(~.sample, ncol = 5) +
  scale_x_continuous(expand = c(0.1, 0.1)) + 
  theme(legend.position = "bottom",
        strip.text = element_text(size = 18, margin = margin(t = 3, b = 3)),
        strip.background = element_rect(color = "black", size = 1.5))

plot_lineup_theirs
```

(ref:lineup-ours) The lineup for the scatter plot.

```{r lineup-ours, fig.height = 10, fig.width = 10, fig.cap = "(ref:lineup-ours)"}
plot_lineup_ours <- ggplot(lineup_data, aes(coverage_obscured * 100, colorectal_cancer_screenings)) +
  geom_point() +  
  geom_smooth(method = loess, formula = y ~ x) +
  facet_wrap(~.sample, ncol = 5) +
  scale_x_continuous(expand = c(0.1, 0.1)) + 
  theme(legend.position = "bottom",
        strip.text = element_text(size = 18, margin = margin(t = 3, b = 3)),
        strip.background = element_rect(color = "black", size = 1.5),
        axis.text = element_blank(),
        axis.title = element_blank(),
        axis.line = element_blank(),
        axis.ticks.length = unit(0, "pt"))

plot_lineup_ours
```



# Same plots with higher associations between variables

The following are plots based on data that purposely modifies cancer screening to induce a higher association with the coverage. This higher association is induced (as shown in the code below) by rearranging data by the coverage and modifying the cancer screening percentage so that it is ordered from low to high. 

```{r data-false, class.source="fold-show"}
df_false <- df_miss %>% 
  arrange(coverage_obscured) %>% 
  mutate(colorectal_cancer_screenings = sort(colorectal_cancer_screenings))

lineup_false_data <- null_permute("colorectal_cancer_screenings") %>% 
  lineup(true = df_false, n = 20, pos = 5)
```

(ref:lineup-theirs-false) Which plot looks the most strikingly different to you?

```{r lineup-theirs-false, fig.height = 10, fig.width = 10, fig.cap = "(ref:lineup-theirs-false)"}
plot_lineup_theirs %+% lineup_false_data
```

(ref:lineup-ours-false) The above shows a lineup for data that was purposely manipulated so that two variables have a higher association. How easy was it to spot the data plot compared to [Figure S5](#fig:lineup-theirs-false)?

```{r lineup-ours-false, fig.height = 10, fig.width = 10, fig.cap = "(ref:lineup-ours-false)"}
plot_lineup_ours %+% lineup_false_data
```


# Acknowledgement 

We thank @basole2021 for supplying us the synthetic data to draw the above plots. 

# Reference

<details>
<summary>Session Information</summary>
```{r session-info}
sessioninfo::session_info()
```
</details>

  
<br>
